Editing Probabilistic Machine Learning (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Uncertainty Estimation Comparison
! Method !! Type of Uncertainty !! Coverage Guarantee !! Computational Cost
|-
| Point estimate + softmax || None (overconfident) || None || Very low
|-
| Temperature scaling || Calibrated confidence || Empirical only || Very low
|-
| MC Dropout || Epistemic (approx) || None || Low
|-
| Deep Ensembles || Both (approx) || None || High
|-
| Conformal prediction || Distribution-free sets || Guaranteed (1-α) || Low
|-
| Gaussian process || Epistemic (exact for GP) || Bayesian || Very high
|}

'''Failure modes''': Overconfident point estimates causing unsafe decisions in high-stakes settings. Poor calibration — confidence scores don't match empirical frequencies. Distribution shift invalidating calibration. VAE posterior collapse — decoder ignores latent code. Conformal prediction requires exchangeable data — fails under distribution shift without adaptation.
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